The World Wide Web provides access to an extraordinary large collection of information sources (in various formats including text, images, videos and other media content) relating to virtually every subject imaginable. As the World Wide Web has grown, the ability of users to search this collection of information and identify content relevant to a particular subject has become increasingly important.
A user of a search engine, for example, typically supplies a query to the search engine that contains only a few terms and expects the search engine to return a result set comprising relevant content items. Although a search engine may return a result set comprising hundreds of relevant content items, most users are likely to only view the top several content items in a result set. Thus, to be useful to a user, a search engine should determine those content items in a given result set that are most relevant to the user, or that the user would be most interested in, on the basis of the query that the user submits and rank such content items accordingly.
A user's view as to which content items are relevant to the query is influenced by a number of factors, many of which are highly subjective. Due to the highly subjective nature of such factors, it is generally difficult to capture in an algorithmic set of rules those factors that define a function for ranking content items. Furthermore, these subjective factors may change over time, as for example when current events are associated with a particular query term. Thus, users who receive search result sets that contain results not perceived to be highly relevant quickly become frustrated and may potentially abandon the use of a particular search engine. Therefore, designing an effective and efficient function that is operative to retrieve and efficiently rank content items is of the upmost importance to information retrieval.